My research interests span programming languages, human factors, and computing education.
I'm interested in programming languages as user interfaces: in short, how language design affects the way people think about and write programs.
Within that space, I'm particularly interested in the usability of static and gradual type systems, and the role of functional programming within computing education.

Projects

Recent escapades in research, development, and coursework.

Ply: Visual Web Inspection

Delta Lab

researchdevelopment

CSS is syntactically straightforward, but has a steep learning curve and complicated semantics. Inspecting the source of existing webpages can help illustrate concepts, but such webpages are typically too complex to serve as useful learning materials. Drawing inspiration from prior research in both software engineering and the learning sciences, we present a new web inspection tool and set of techniques for pruning irrelevant CSS and identifying implicit dependencies between properties. Supervised by Haoqi Zhang and Nell O’Rourke. Honorable Mention Paper at UIST 2018, Berlin.

Evaluating peer graders

Northwestern University

research

Most of the literature on peer grading focuses on inferring a true grade from a set of noisy reports. We study a different problem: inferring the skill and effort of reviewers, from the same reports. Supervised by Jason Hartline.

Guiding Web Inspection with Tutorial Keyword Frequency

Delta Lab

researchdevelopment

In order to bridge the gap between web design tutorials and real-world examples, we extend a web inspector to highlight CSS properties frequently mentioned across a given set of tutorials. Google Scholars’ Retreat 2016, Mountain View, California.

SVG Charting Library

LinkedIn

development

An opinionated Ember.js addon to replace Highcharts with native SVG and DOM APIs. Released addon as a company-wide multiproduct. I worked on this project during my internship at LinkedIn, under the mentorship of Cody Coats and Michail Yasonik.

Predicting the Popularity of User-Generated Discussion Questions

EECS 349: Machine Learning

courseworkdevelopment

Using Python with the Reddit API and NLTK library, we collect information about AskReddit posts over a two-week period to analyze what makes a question popular. Alternating decision trees achieve 72.9819 accuracy with 10-fold cross-validation, an improvement over the ZeroR baseline of 51.0708. Features related to the language of the question, time and day of posting, and initial commenting behavior prove most informative. With Sameer Srivastava, Jennie Werner, and Aiqi Liu.

Commentator

Northwestern Debate Institute

development

End-to-end Google Apps Script-based pipeline for publishing practice debate comments to individual students’ feedback pages. Previously, instructors needed to manually edit the feedback pages for all four students in order to provide feedback from practice rounds. Deployed at the 2015 Northwestern Debate Institute and subsequently adopted for the entire program in 2016.